scholarly journals Elucidation of Novel Structural Scaffold in Rohu TLR2 and Its Binding Site Analysis with Peptidoglycan, Lipoteichoic Acid and Zymosan Ligands, and Downstream MyD88 Adaptor Protein

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Bikash Ranjan Sahoo ◽  
Madhubanti Basu ◽  
Banikalyan Swain ◽  
Manas Ranjan Dikhit ◽  
Pallipuram Jayasankar ◽  
...  
Keyword(s):  
Ligand Binding ◽  
Interleukin 1 ◽  
Adaptor Protein ◽  
Comparative Modeling ◽  
Lipoteichoic Acid ◽  
3D Models ◽  
Dynamics Simulation ◽  
Binding Motifs ◽  
Downstream Signaling ◽  
Binding Domains

Toll-like receptors (TLRs) play key roles in sensing wide array of microbial signatures and induction of innate immunity. TLR2 in fish resembles higher eukaryotes by sensing peptidoglycan (PGN) and lipoteichoic acid (LTA) of bacterial cell wall and zymosan of yeasts. However, in fish TLR2, no study yet describes the ligand binding motifs in the leucine rich repeat regions (LRRs) of the extracellular domain (ECD) and important amino acids in TLR2-TIR (toll/interleukin-1 receptor) domain that could be engaged in transmitting downstream signaling. We predicted these in a commercially important freshwater fish species rohu (Labeo rohita) by constructing 3D models of TLR2-ECD, TLR2-TIR, and MyD88-TIR by comparative modeling followed by 40 ns (nanosecond) molecular dynamics simulation (MDS) for TLR2-ECD and 20 ns MDS for TLR2-TIR and MyD88-TIR. Protein (TLR2-ECD)–ligands (PGN, LTA, and zymosan) docking in rohu by AutoDock4.0, FlexX2.1, and GOLD4.1 anticipated LRR16–19, LRR12–14, and LRR20-CT as the most important ligand binding motifs. Protein (TLR2-TIR)—protein (MyD88-TIR) interaction by HADDOCK and ZDOCK predicted BB loop,αB-helix,αC-helix, and CD loop in TLR2-TIR and BB loop,αB-helix, and CD loop in MyD88-TIR as the critical binding domains. This study provides ligands recognition and downstream signaling.

scholarly journals Cooperative Interaction of Nck and Lck Orchestrates Optimal TCR Signaling

Cells ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 834
Author(s):  
Frederike A. Hartl ◽  
Jatuporn Ngoenkam ◽  
Esmeralda Beck-Garcia ◽  
Liz Cerqueira ◽  
Piyamaporn Wipa ◽  
...  
Keyword(s):  
T Cell ◽  
Ligand Binding ◽  
T Cell Activation ◽  
Cell Activation ◽  
Adaptor Protein ◽  
Cooperative Interaction ◽  
Tcr Signaling ◽  
Binding Motifs ◽  
Adaptive Immune

The T cell antigen receptor (TCR) is expressed on T cells, which orchestrate adaptive immune responses. It is composed of the ligand-binding clonotypic TCRαβ heterodimer and the non-covalently bound invariant signal-transducing CD3 complex. Among the CD3 subunits, the CD3ε cytoplasmic tail contains binding motifs for the Src family kinase, Lck, and the adaptor protein, Nck. Lck binds to a receptor kinase (RK) motif and Nck binds to a proline-rich sequence (PRS). Both motifs only become accessible upon ligand binding to the TCR and facilitate the recruitment of Lck and Nck independently of phosphorylation of the TCR. Mutations in each of these motifs cause defects in TCR signaling and T cell activation. Here, we investigated the role of Nck in proximal TCR signaling by silencing both Nck isoforms, Nck1 and Nck2. In the absence of Nck, TCR phosphorylation, ZAP70 recruitment, and ZAP70 phosphorylation was impaired. Mechanistically, this is explained by loss of Lck recruitment to the stimulated TCR in cells lacking Nck. Hence, our data uncover a previously unknown cooperative interaction between Lck and Nck to promote optimal TCR signaling.

Pococurante (Poc): An ENU-Induced Missense Mutation of MyD88 with Selective Effects on TLR Signal Transduction.

Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 649-649
Author(s):  
Zhengfan Jiang ◽  
Chenglong Li ◽  
Louis Shamel ◽  
Arthur Olson ◽  
Bruce Beutler
Keyword(s):  
Signal Transduction ◽  
Interleukin 1 ◽  
Adaptor Protein ◽  
Structural Diversity ◽  
Lipoteichoic Acid ◽  
Protein Docking ◽  
Structural Basis ◽  
Cpg Dna ◽  
Cpg Dinucleotides ◽  
Tir Domain

Abstract Toll-like receptors (TLRs) are key sensors of the innate immune system, and individual TLRs respond to specific molecules derived from microbes. MyD88 is a Toll/Interleukin-1/Resistance (TIR) domain-containing adaptor protein required for signaling by all TLRs except TLR3. While the structural basis of association between MyD88 and TIR-domain receptors is obscure, MyD88-deficient mice show no responses to bacterial flagellin, peptidoglycan (PGN), lipoteichoic acid (LTA), bacterial lipopeptides such as PAM2CSK4, PAM3CSK4 and R- or S-MALP-2, DNA bearing unmethylated CpG dinucleotides (CpG DNA), or Resiquimod (RSQ). Using germline ENU mutagenesis, we have produced a large number of phenotypic variants that have abnormal TLR signaling. We now report the identification of a new mutation called Pococurante (Poc), originally detected in screening because macrophages from this mouse showed no response to the tri-acylated lipopeptide PAM3CSK4, the di-acylated lipopeptide S-MALP-2, LTA, CpG DNA, RSQ, and a markedly reduced response to LPS: the ligands for TLRs 2/1, 2/6, 9, 7 and 4 respectively. They also had no response to interleukin-1, a cytokine that signals by way of a MyD88-dependent TIR domain receptor. However, Poc mice showed a normal response to PGN, as well as R-MALP-2 and PAM2CSK4 lipopeptides. The latter three ligands are sensed in a TLR2-dependent, MyD88-dependent fashion. The Poc phenotype was ascribed to a point mutation of MyD88 affecting a surface residue (I179N). Because the mutation is discriminatory, permitting MyD88 to carry a signal from some TIR domain receptors but not others, we infer that it resides at the receptor:adaptor signaling interface. A new model of TIR receptor:adaptor interaction is proposed on the basis of docking studies that take account of the Poc phenotype, made using the protein-protein docking program SURFDOCK. We note that S-MALP-2 is dependent upon TLR2/6 heterodimers, while PAM3CSK4 sensing depends upon TLR2/1 heterodimers. Since the Poc mutation forbids detection of both these ligands while it allows detection of PAM2CSK4 and R-MALP-2, it may be inferred that TLR2 signal transduction entails greater structural diversity than was previously supposed. The involvement of TLR2 homodimers, or the incorporation of subunits yet unknown into the receptor complex, cannot be excluded.

scholarly journals Discovery of pan-VEGF inhibitory peptides directed to the extracellular ligand-binding domains of the VEGF receptors

2016 ◽  
Vol 2 (10) ◽  
pp. e1600611 ◽  
Author(s):  
Jussara S. Michaloski ◽  
Alexandre R. Redondo ◽  
Leila S. Magalhães ◽  
Caio C. Cambui ◽  
Ricardo J. Giordano
Keyword(s):  
Ligand Binding ◽  
Small Molecule ◽  
Tyrosine Kinases ◽  
Family Members ◽  
Binding Pocket ◽  
Vegf Receptors ◽  
Vegf Inhibitors ◽  
Downstream Signaling ◽  
Binding Domains ◽  
Vegf Family Members

Receptor tyrosine kinases (RTKs) are key molecules in numerous cellular processes, the inhibitors of which play an important role in the clinic. Among them are the vascular endothelial growth factor (VEGF) family members and their receptors (VEGFR), which are essential in the formation of new blood vessels by angiogenesis. Anti-VEGF therapy has already shown promising results in oncology and ophthalmology, but one of the challenges in the field is the design of specific small-molecule inhibitors for these receptors. We show the identification and characterization of small 6-mer peptides that target the extracellular ligand-binding domain of all three VEGF receptors. These peptides specifically prevent the binding of VEGF family members to all three receptors and downstream signaling but do not affect other angiogenic RTKs and their ligands. One of the selected peptides was also very effective at preventing pathological angiogenesis in a mouse model of retinopathy, normalizing the vasculature to levels similar to those of a normal developing retina. Collectively, our results suggest that these peptides are pan-VEGF inhibitors directed at a common binding pocket shared by all three VEGFRs. These peptides and the druggable binding site they target might be important for the development of novel and selective small-molecule, extracellular ligand-binding inhibitors of RTKs (eTKIs) for angiogenic-dependent diseases.

Reversibly Sampling Conformations and Binding Modes Using Molecular Darting

2020 ◽  
Author(s):  
Samuel C. Gill ◽  
David Mobley
Keyword(s):  
Monte Carlo ◽  
Ligand Binding ◽  
Binding Sites ◽  
Degrees Of Freedom ◽  
Dynamics Simulation ◽  
Hiv Integrase ◽  
Binding Modes ◽  
System A ◽  
Multiple Binding ◽  
Internal Degrees Of Freedom

<div>Sampling multiple binding modes of a ligand in a single molecular dynamics simulation is difficult. A given ligand may have many internal degrees of freedom, along with many different ways it might orient itself a binding site or across several binding sites, all of which might be separated by large energy barriers. We have developed a novel Monte Carlo move called Molecular Darting (MolDarting) to reversibly sample between predefined binding modes of a ligand. Here, we couple this with nonequilibrium candidate Monte Carlo (NCMC) to improve acceptance of moves.</div><div>We apply this technique to a simple dipeptide system, a ligand binding to T4 Lysozyme L99A, and ligand binding to HIV integrase in order to test this new method. We observe significant increases in acceptance compared to uniformly sampling the internal, and rotational/translational degrees of freedom in these systems.</div>

Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes Using Nonequilibrium Candidate Monte Carlo

2017 ◽  
Author(s):  
Samuel Gill ◽  
Nathan M. Lim ◽  
Patrick Grinaway ◽  
Ariën S. Rustenburg ◽  
Josh Fass ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Ligand Binding ◽  
Binding Mode ◽  
Free Energy Calculations ◽  
Dynamics Simulation ◽  
Binding Modes ◽  
Enhanced Sampling ◽  
Recent Success ◽  
Multiple Binding ◽  
Proper Sampling

<div>Accurately predicting protein-ligand binding is a major goal in computational chemistry, but even the prediction of ligand binding modes in proteins poses major challenges. Here, we focus on solving the binding mode prediction problem for rigid fragments. That is, we focus on computing the dominant placement, conformation, and orientations of a relatively rigid, fragment-like ligand in a receptor, and the populations of the multiple binding modes which may be relevant. This problem is important in its own right, but is even more timely given the recent success of alchemical free energy calculations. Alchemical calculations are increasingly used to predict binding free energies of ligands to receptors. However, the accuracy of these calculations is dependent on proper sampling of the relevant ligand binding modes. Unfortunately, ligand binding modes may often be uncertain, hard to predict, and/or slow to interconvert on simulation timescales, so proper sampling with current techniques can require prohibitively long simulations. We need new methods which dramatically improve sampling of ligand binding modes. Here, we develop and apply a nonequilibrium candidate Monte Carlo (NCMC) method to improve sampling of ligand binding modes.</div><div><br></div><div>In this technique the ligand is rotated and subsequently allowed to relax in its new position through alchemical perturbation before accepting or rejecting the rotation and relaxation as a nonequilibrium Monte Carlo move. When applied to a T4 lysozyme model binding system, this NCMC method shows over two orders of magnitude improvement in binding mode sampling efficiency compared to a brute force molecular dynamics simulation. This is a first step towards applying this methodology to pharmaceutically relevant binding of fragments and, eventually, drug-like molecules. We are making this approach available via our new Binding Modes of Ligands using Enhanced Sampling (BLUES) package which is freely available on GitHub.</div>

Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes Using Nonequilibrium Candidate Monte Carlo

2018 ◽  
Author(s):  
Samuel Gill ◽  
Nathan M. Lim ◽  
Patrick Grinaway ◽  
Ariën S. Rustenburg ◽  
Josh Fass ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Ligand Binding ◽  
Binding Mode ◽  
Free Energy Calculations ◽  
Dynamics Simulation ◽  
Binding Modes ◽  
Enhanced Sampling ◽  
Recent Success ◽  
Multiple Binding ◽  
Proper Sampling

<div>Accurately predicting protein-ligand binding is a major goal in computational chemistry, but even the prediction of ligand binding modes in proteins poses major challenges. Here, we focus on solving the binding mode prediction problem for rigid fragments. That is, we focus on computing the dominant placement, conformation, and orientations of a relatively rigid, fragment-like ligand in a receptor, and the populations of the multiple binding modes which may be relevant. This problem is important in its own right, but is even more timely given the recent success of alchemical free energy calculations. Alchemical calculations are increasingly used to predict binding free energies of ligands to receptors. However, the accuracy of these calculations is dependent on proper sampling of the relevant ligand binding modes. Unfortunately, ligand binding modes may often be uncertain, hard to predict, and/or slow to interconvert on simulation timescales, so proper sampling with current techniques can require prohibitively long simulations. We need new methods which dramatically improve sampling of ligand binding modes. Here, we develop and apply a nonequilibrium candidate Monte Carlo (NCMC) method to improve sampling of ligand binding modes.</div><div><br></div><div>In this technique the ligand is rotated and subsequently allowed to relax in its new position through alchemical perturbation before accepting or rejecting the rotation and relaxation as a nonequilibrium Monte Carlo move. When applied to a T4 lysozyme model binding system, this NCMC method shows over two orders of magnitude improvement in binding mode sampling efficiency compared to a brute force molecular dynamics simulation. This is a first step towards applying this methodology to pharmaceutically relevant binding of fragments and, eventually, drug-like molecules. We are making this approach available via our new Binding Modes of Ligands using Enhanced Sampling (BLUES) package which is freely available on GitHub.</div>

scholarly journals The effect of protein mutations on drug binding suggests ensuing personalised drug selection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shunzhou Wan ◽  
Deepak Kumar ◽  
Valentin Ilyin ◽  
Ussama Al Homsi ◽  
Gulab Sher ◽  
...  
Keyword(s):  
Estrogen Receptor ◽  
Ligand Binding ◽  
Binding Free Energy ◽  
Personalised Medicine ◽  
Clinical Decision ◽  
Drug Binding ◽  
Dynamics Simulation ◽  
Breast Cancer Patients ◽  
Natural Ligand ◽  
Affinity Interaction

AbstractThe advent of personalised medicine promises a deeper understanding of mechanisms and therefore therapies. However, the connection between genomic sequences and clinical treatments is often unclear. We studied 50 breast cancer patients belonging to a population-cohort in the state of Qatar. From Sanger sequencing, we identified several new deleterious mutations in the estrogen receptor 1 gene (ESR1). The effect of these mutations on drug treatment in the protein target encoded by ESR1, namely the estrogen receptor, was achieved via rapid and accurate protein–ligand binding affinity interaction studies which were performed for the selected drugs and the natural ligand estrogen. Four nonsynonymous mutations in the ligand-binding domain were subjected to molecular dynamics simulation using absolute and relative binding free energy methods, leading to the ranking of the efficacy of six selected drugs for patients with the mutations. Our study shows that a personalised clinical decision system can be created by integrating an individual patient’s genomic data at the molecular level within a computational pipeline which ranks the efficacy of binding of particular drugs to variant proteins.

scholarly journals Paired opposing leukocyte receptors recognizing rapidly evolving ligands are subject to homogenization of their ligand binding domains

2011 ◽  
Vol 63 (12) ◽  
pp. 809-820 ◽  
Author(s):  
Sigbjørn Fossum ◽  
Per Christian Saether ◽  
John Torgils Vaage ◽  
Michael Rory Daws ◽  
Erik Dissen
Keyword(s):  
Ligand Binding ◽  
Binding Domains ◽  
Leukocyte Receptors
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